 - Reinforcement Learning (RL) [1]}. We adapt Model-based Policy Optimization (MBPO) [1]}, one of the most popular model-based RL (MBRL) algorithm as our offline RL baseline. We first learn the latent space and then use the L2-distance in the latent space as a reward for the RL algorithm. The farther a state is from the final goal in the latent space, the lower the reward is. It can be seen that, without Transformer architecture, MBRL performs not as well as our model.
